The ways how to evaluate facial attractiveness complexly and how to make comparisons between facial images of patients before and after facial plastic surgery procedure are still unclear and require ongoing research.

In this study, we have developed a web-based shiny application providing facial image processing, both manual and automated landmarking, facial geometry computations and machine-learning models allowing to identify geometric facial features associated with an increase of facial attractiveness after undergoing rhinoplasty, common facial plastic surgery.

Patients’ facial image data were processed, landmarked and analysed using the application. Facial attractiveness was measured using Likert scale by a board of independent observers. Machine-learning built-in approaches were performed to select predictors increasing facial attractiveness after undergoing rhinoplasty.

The shiny web framework enables to develop a complex web interface including HTML, CSS and javascript front-end and R-based back-end bridging C++ library dlib which performs image computations. In addition, the connected shinyjs package offers a user-server clickable interaction useful for the landmarking.

keywords: shiny, R, machine learning, facial attractiveness, plastic surgery